



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				str					  =       args.log_outputs
							_lowerCAmelCase	:				Tuple					  =       "_".join(args.dataset.split("/"    ) + [args.config, args.split]    )

							# load metric
							_lowerCAmelCase	:				List[Any]					  =       load_metric("wer"    )
							_lowerCAmelCase	:				str					  =       load_metric("cer"    )

							# compute metrics
							_lowerCAmelCase	:				str					  =       wer.compute(references=result["target"]							,					predictions=result["prediction"]    )
							_lowerCAmelCase	:				Optional[int]					  =       cer.compute(references=result["target"]							,					predictions=result["prediction"]    )

							# print & log results
							_lowerCAmelCase	:				Dict					  =       F"WER: {wer_result}\nCER: {cer_result}"
							print(a_    )

							with open(F"{dataset_id}_eval_results.txt"							,					"w"    ) as f:
														f.write(a_    )

							# log all results in text file. Possibly interesting for analysis
							if log_outputs is not None:
														_lowerCAmelCase	:				int					  =       F"log_{dataset_id}_predictions.txt"
														_lowerCAmelCase	:				Optional[int]					  =       F"log_{dataset_id}_targets.txt"

														with open(a_							,					"w"    ) as p, open(a_							,					"w"    ) as t:
																					# mapping function to write output
																					def write_to_file(_lowerCamelCase							,					_lowerCamelCase    ):
																												p.write(F"{i}" + "\n"    )
																												p.write(batch["prediction"] + "\n"    )
																												t.write(F"{i}" + "\n"    )
																												t.write(batch["target"] + "\n"    )

																					result.map(a_							,					with_indices=a_    )






def 							A							(   _lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				List[Any]					  =       "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

							_lowerCAmelCase	:				Union[str, Any]					  =       re.sub(a_							,					""							,					text.lower()    )

							# In addition, we can normalize the target text, e.g. removing new lines characters etc...
							# note that order is important here!
							_lowerCAmelCase	:				Any					  =       ["\n\n", "\n", "   ", "  "]

							for t in token_sequences_to_ignore:
														_lowerCAmelCase	:				str					  =       " ".join(text.split(a_    )    )

							return text






def 							A							(   _lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				Union[str, Any]					  =       load_dataset(args.dataset							,					args.config							,					split=args.split							,					use_auth_token=a_    )

							# for testing: only process the first two examples as a test
							# dataset = dataset.select(range(10))

							# load processor
							_lowerCAmelCase	:				Optional[int]					  =       AutoFeatureExtractor.from_pretrained(args.model_id    )
							_lowerCAmelCase	:				List[str]					  =       feature_extractor.sampling_rate

							# resample audio
							_lowerCAmelCase	:				str					  =       dataset.cast_column("audio"							,					Audio(sampling_rate=a_    )    )

							# load eval pipeline
							if args.device is None:
														_lowerCAmelCase	:				Tuple					  =       0 if torch.cuda.is_available() else -1
							_lowerCAmelCase	:				Optional[Any]					  =       pipeline("automatic-speech-recognition"							,					model=args.model_id							,					device=args.device    )

							# map function to decode audio
							def map_to_pred(_lowerCamelCase    ):
														_lowerCAmelCase	:				List[Any]					  =       asr(
														    batch["audio"]["array"]							,					chunk_length_s=args.chunk_length_s							,					stride_length_s=args.stride_length_s    )

														_lowerCAmelCase	:				Optional[Any]					  =       prediction["text"]
														_lowerCAmelCase	:				Optional[Any]					  =       normalize_text(batch["sentence"]    )
														return batch

							# run inference on all examples
							_lowerCAmelCase	:				Tuple					  =       dataset.map(a_							,					remove_columns=dataset.column_names    )

							# compute and log_results
							# do not change function below
							log_results(a_							,					a_    )


if __name__ == "__main__":
						_snake_case						=    argparse.ArgumentParser()

						parser.add_argument(
						    "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
						)
						parser.add_argument(
						    "--dataset",
						    type=str,
						    required=True,
						    help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
						)
						parser.add_argument(
						    "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
						)
						parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
						parser.add_argument(
						    "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
						)
						parser.add_argument(
						    "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
						)
						parser.add_argument(
						    "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
						)
						parser.add_argument(
						    "--device",
						    type=int,
						    default=None,
						    help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
						)
						_snake_case						=    parser.parse_args()

						main(args)







'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



